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Journal of Information Systems Engineering and Business Intelligence
Published by Universitas Airlangga
ISSN : -     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal ini menerima makalah ilmiah dengan fokus pada Rekayasa Sistem Informasi ( Information System Engineering) dan Sistem Bisnis Cerdas (Business Intelligence) Rekayasa Sistem Informasi ( Information System Engineering) adalah Pendekatan multidisiplin terhadap aktifitas yang berkaitan dengan pengembangan dan pengelolaan sistem informasi dalam pencapaian tujuan organisasi. ruang lingkup makalah ilmiah Information Systems Engineering meliputi (namun tidak terbatas): -Pengembangan, pengelolaan, serta pemanfaatan Sistem Informasi. -Tata Kelola Organisasi, -Enterprise Resource Planning, -Enterprise Architecture Planning, -Knowledge Management. Sistem Bisnis Cerdas (Business Intelligence) Mengkaji teknik untuk melakukan transformasi data mentah menjadi informasi yang berguna dalam pengambilan keputusan. mengidentifikasi peluang baru serta mengimplementasikan strategi bisnis berdasarkan informasi yang diolah dari data sehingga menciptakan keunggulan kompetitif. ruang lingkup makalah ilmiah Business Intelligence meliputi (namun tidak terbatas): -Data mining, -Text mining, -Data warehouse, -Online Analytical Processing, -Artificial Intelligence, -Decision Support System.
Arjuna Subject : -
Articles 283 Documents
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The Influence of Augmented Reality Technology on Young Consumers’ Online Purchasing Decisions Nguyen Thi Hoi
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.520-533

Abstract

Background: Augmented reality (AR) has emerged as a transformative technology in e-commerce, enabling immersive, interactive, and personalized shopping experiences that help bridge the gap between online and offline retail environments. Although the adoption of AR has gained increasing attention in digital commerce, empirical evidence regarding the determinants of AR-enabled purchasing behavior remains limited, particularly among young consumers in emerging digital economies. Objective: This study aims to examine the factors that influence AR-enabled online purchasing decisions and identify the technological, psychological, social, and behavioral drivers that encourage consumers to adopt AR-supported shopping experiences. The proposed framework is illustrated through a survey conducted among young consumers in Hanoi, Vietnam, to provide empirical evidence. Methods: An integrated research framework was developed based on the TRA, TPB, UTAUT2, and social impact models. We employed a quantitative approach using data collected from 400 respondents who had prior experience using AR features in online shopping. SEM was applied to test the proposed hypotheses and evaluate the relationships among the constructs. Results: The findings indicate that Habit, Belief, Satisfaction, Social Influence, Facilitating Conditions, Perceived Number of Users, Subjective Norms, Effort Expectancy, and Attitude significantly influence consumers’ behavioral intention and purchase decision. In addition, BI serves as a significant mediating mechanism through which AR adoption factors are translated into actual purchasing behavior. Conclusion: By providing a comprehensive framework for understanding consumer adoption of AR technologies in online shopping environments, this study contributes to the growing literature on AR-enabled commerce. The findings offer practical implications for e-commerce platforms, digital retailers, and technology providers seeking to enhance customer engagement, trust, and purchase conversion through AR-enabled shopping experiences. The empirical evidence from Vietnam further enriches the understanding of AR adoption in emerging digital markets while supporting the proposed framework’s broader applicability across e-commerce contexts.   Keywords: Augmented Reality, online purchasing decisions, young consumers, behavioral intention, digital commerce
A Dual-Stream CNN and Trajectory-Transformer Model for Early Dysgraphia Screening Using Handwritten Data Yuri Pamungkas; Abdul Karim; Muhammad Nur Afnan Uda; Uda Hashim
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.253-268

Abstract

Background: Dysgraphia is a learning disorder that commonly affects handwriting fluency and legibility through difficulties in motor coordination and spatial organization. Early identification is needed to support timely intervention, but conventional assessment remains dependent on subjective observation and manual evaluation, making it difficult to apply efficiently at scale. To address this problem, this study proposes a dual-stream convolutional neural network and trajectory-transformer model for automated early dysgraphia screening by combining spatial and temporal handwriting features Objective: This study develops an end-to-end multimodal deep learning framework that integrates image- and trajectory-based handwriting representations to support accurate and interpretable dysgraphia classification. Methods: The proposed model contains two complementary streams. The CNN stream extracts spatial handwriting features, including stroke shape, character structure, and alignment, whereas the transformer stream models temporal movement patterns, such as stroke rhythm and writing velocity. These representations are combined through an attention-based fusion mechanism to produce a unified SEM. The model was trained and evaluated using the Potential Dysgraphia Handwriting Dataset, which includes 249 labeled handwriting samples categorized as low and potential dysgraphia. Results: The model achieved an overall accuracy of 95.9%, with precision, recall, and F1-score values of approximately 0.96 and an area under the curve (AUC) of 0.997. It also outperformed single-stream baseline models. Grad–CAM and attention map visualizations showed that the model focused on dysgraphia-associated handwriting regions and ischemic stroke patterns. The t–SNE projection of fused features showed clear separation between the two classes, indicating that the learned embeddings contained discriminative spatial and temporal information. Conclusion: The dual-stream convolutional neural network and trajectory-transformer model provides an accurate and explainable approach for early dysgraphia detection. The framework offers a data-driven basis for objective handwriting assessment in educational and clinical settings by linking the visual structure of handwriting with the movement process used to produce it.   Keywords: Dysgraphia screening, Handwriting analysis, Convolutional Neural Network (CNN), Transformer, Multimodal deep learning
A Lightweight Landmark-Based Model for Bahasa Lip-Reading Using Attention-BiLSTM Ferzha Putra Utama; Risanuri Hidayat; Syukron Abu Ishaq Alfarozi
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.269-282

Abstract

Background: Most lip-reading studies primarily utilize image-based representations of lip movements, offering extensive visual data while imposing significant computational burdens. Lip landmark-based representations are still not well understood, even though they could be a better way to describe lip dynamics in a smaller and more efficient way. This limitation is even more apparent in Bahasa lip-reading research, where there are few studies and computationally efficient solutions remain essential. Objective: This study examines the shortcomings of image-based lip-reading methods by leveraging lip landmarks as a concise and computationally efficient input representation. The proposed method is tested on the IndoLR open dataset, which contains video data of lip-reading in Bahasa. Methods: In this study, video sequences were transformed into coordinate-based landmark data to minimize computational demands while preserving critical information regarding lip dynamics. An attention-based BiLSTM model was trained to group10 word classes and 4 phrase classes using this dataset. Results: The model achieved accuracies of 93.03% for word classification and 95.18% for phrase classification. The approach also maintained high efficiency, with average inference times of 0.000530 and 0.011366 s per sample and computational costs of only 0.01 and 0.14 GFLOPs, respectively. Conclusion: These results show how well lip landmarks can be combined with a lightweight deep learning model with very few resources. This study makes a significant contribution to research on lip-reading in Bahasa and lays the groundwork for future studies that will use larger and more diverse datasets.   Keywords: Attention, BiLSTM, Bahasa, Landmark, Lip-Reading
The Effect of The Use of AI on Creativity in Creative Companies Hamza Bami; Yang Jing Zhao; Zhu Xun; Muneeb Ali
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.361-373

Abstract

Background: The rapid use of artificial intelligence (AI) in the creative industries has raised some questions about whether it will have any effects on human creativity and innovative practices. Recent studies highlight the possibility of AI to enhance creative output and the impact of originality and human agency. Objective: This study uses the technology acceptance model (TAM) to assess the role played by the implementation of AI in the creative process in creative companies. The role of perceived usefulness, perceived ease of use, behavioral intention to use AI, and trust in AI as a moderating variable is investigated. Methods: The study was based on a quantitative methodology, where data were collected from 720 creative companies in China. A well-designed questionnaire based on the literature was sent to the professionals, and the data were analyzed. The proposed relationships were analyzed with the help of SmartPLS. Results: Preliminary results show that perceived usefulness and ease of use positively contribute to employee attitudes toward AI, which, in turn, positively impact their intention to use AI and eventually lead to high levels of creativity. However, trust in AI had no significant effect in moderating the relationship between attitude toward using and behavioral intention. Conclusion: These findings indicate that the development of positive attitudes toward AI and its adoption can lead to the enhancement of creativity in an organization that works in a highly innovative industry. This study contributes to the growing literature on AI and creativity and offers practical implications for managers and policymakers who plan to use AI as a creative performance tool. Further studies can also be based on these findings to explore other variables, additional contexts, and long-term outcomes to expand the knowledge of AI’s role in forming creativity.   Keywords: Artificial intelligence (AI), Technology Acceptance Model (TAM), Creative industries, Creativity, PLS-SEM
A Systematic Review on Digital Investment Platform Adoption Through the Lens of Technology, Risk, Trust, Quality, and User Experience Dimensions Karisma Nabil Santosa; Reny Nadlifatin; Apol Pribadi Subriadi
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.236-252

Abstract

Background: Digital investment platforms are growing rapidly, accompanied by great interest in adoption from the younger generation who are accustomed to using technology. However, existing research is still limited to instrument-specific analyses and not platform-specific ones, thus limiting a comprehensive understanding of broader adoption determinants. Objective: This study systematically reviews empirical research on the adoption of digital investment platforms by identifying the types of platforms studied, the most frequently used theoretical models, and the key determinant thematic groups influencing adoption across investment platforms. Methods: The Preferred Reporting Items for Systematic Literature Review guidelines were used to conduct the systematic literature review. VOSviewer was also used for bibliometric analysis to identify trends through keyword co-occurrences. Of the total 7,727 articles, 42 studies collected from 2020 to 2025 resulted in 42 studies that met the inclusion criteria. Results: The included literature focused on stock, cryptocurrencies, peer-to-peer lending, crowdfunding, mutual funds, and e-gold platforms. The most widely used theoretical frameworks were the TAM- and UTAUT-based models. The thematic synthesis revealed five key determinants that influence the adoption of digital investment platforms: technology adoption, risk, trust, quality, and user experience. Conclusion: This research demonstrates that the included literature forms an integrative framework for digital platform investment instruments that reveals that risk, trust, quality, and user experience are interrelated rather than a stand-alone dimension. This is often underemphasized and fragmented in previous adoption models. The new framework provides a foundation for future empirical validation, particularly for investment platforms that offer multiple instruments within a single digital service.   Keywords: Digital Investment Platforms, User Adoption, Risk, Trust, Quality, Systematic Literature Review
An NLP-Based Framework for Requirement Elicitation from Heterogeneous Online Sources Arya Prasetya; Anindya Wita Wisesa; Eva Hariyanti; Nania Nuzulita; Aditya Nugroho; Afifah Nurrosyidah; Indra Kharisma Raharjana
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.443-457

Abstract

Background: Data-driven requirement elicitation has been increasingly used in modern software engineering due to the growing availability of online user-generated textual data. However, existing approaches mostly rely on single-source data, which are limited in handling the diversity of characteristics found in online textual sources. Objective: This study proposes an automated natural language processing (NLP)-based framework for requirement elicitation that integrates heterogeneous online sources, such as app reviews, online news, and tweets, for process innovation in the requirements engineering phase. Methods: The proposed framework combines rule-based and AI-based extraction methods, semantic clustering, and diagram generation. Data were collected from application reviews, Twitter/X, and online news across six domains. The framework was evaluated using expert-annotated ground truth to measure extraction performance and expert-based assessments to examine clustering quality and artifact usefulness. Result: AI-based extraction outperforms rule-based methods for requirements extraction, achieving F1 scores of 0.92, 0.80, and 0.67 on app reviews, 0.80 on Twitter, and 0.67 on online news. In the expert evaluation, the proposed system demonstrates high topic coherence, reduces elicitation time, and helps identify potential system requirements that may be overlooked in manual processes. Conclusion: Multisource integration enhances the completeness and contextual richness of automated requirement elicitation. The proposed framework effectively transforms heterogeneous textual data into actionable requirement artifacts, providing a scalable and practical solution for early-stage software development.   Keywords: Requirement Elicitation, Natural Language Processing, Process Innovation, Multisource Data
Artificial Intelligence Implementation in Banks: A Systematic Literature Review and Future Research Directions Kaoutar Tafali; Marouane Bejjaj; Taoufik Benkaraache
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.374-391

Abstract

Background: Implementation of artificial intelligence (AI) in banks becomes a necessity not only to gain competitiveness, but also to defend these financial institutions against emerging fintech firms. Many literature reviews related to AI implementation in banks have been conducted recently. Despite the fresh insights provided by the findings of these reviews, a comprehensive and complete study on AI adoption remains lacking, particularly a study that includes and synthesizes its antecedents, challenges, impacts, and success factors in banks. Objective: This study analyzes the antecedents, challenges, consequences, and guidelines related to AI implementation in banks. This study also seeks to identify the best practices recommended for policymakers and government authorities to support the adoption of this technology in the banking sector. Methods: A Systematic Literature Review using the Scopus and Web of Science databases, identified 42 relevant articles published between 2020 and May 2025. The research proposes a conceptual framework based on the IPO model, including antecedents, challenges, consequences and guidelines to deepen the understanding of AI implementation in banks and synthesize the current state of related knowledge. Results: The results revealed that the drivers and challenges are categorized into organizational, technological, and environmental topics based on the TOE and DOI theories, the consequences and guidelines provided to banks are categorized into organizational, technological, environmental, and people topics, and the guidelines provided to policymakers and government authorities are classified into AI governance, collaboration, and regulatory support topics.  Conclusion: This study provides both theoretical and practical contributions, by offering a comprehensive analysis of AI implementation in banks—a perspective that is lacking in the actual literature. It can also serve as a valuable guide for bank managers during the implementation process. In addition, the study proposes a research agenda for future research on AI implementation in banks.   Keywords: Artificial Intelligence, Banks, Financial sector, Banking sector, Input-Process-Output, Technology implementation
Analyzing User Intention to Adopt Balinese Script Digital Tool: A Technology Acceptance Model Approach Cokorda Pramartha; Madek Jeani Purnama; Ida Bagus Ary Indra Iswara; I Putu Gede Hendra Suputra; I Wayan Arka; Ni Luh Watiniasih
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.474-488

Abstract

Background: Digital tools can help local scripts remain visible and usable in everyday communication. For that to happen, however, the tools must be accepted by users, not merely made available. In the case of Balinese script, evidence on what encourages people to use digital writing tools is still limited, particularly in relation to perceived usefulness and ease of use. Objective: This study examined the factors that influence users’ intention to adopt the Balinese Script Keyboard. Analyses were made based on the Technology Acceptance Model, more specifically, Perceived Usefulness and Perceived Ease of Use. Methods: A typing task followed by a questionnaire was conducted with 109 respondents in Bali. The data were analyzed using partial least squares structural equation modeling (PLS-SEM) in SmartPLS. The aspects on which the analysis was built are measurement-model assessment, covering reliability, convergent validity through average variance extracted (AVE), and discriminant validity, followed by structural-model testing using bootstrapping. Results: Respondents in general showed positive perceptions of the keyboard, with construct mean scores of 5.29 for PU, 5.26 for PEOU, and 5.32 for Usage Intention (INT). The measurement model met the required validity and reliability criteria, with outer loadings ranging from 0.872 to 0.994, AVE values from 0.826 to 0.974, and Cronbach’s alpha values from 0.958 to 0.987. The structural model also supported all proposed relationships. A significant positive effect on INT was found with PU (β = 0.513, t = 21.787, p < 0.001); similarly, PEOU also had a significant positive effect on INT (β = 0.537, t = 20.724, p < 0.001), and lastly, PEOU had a strong positive effect on PU (β = 0.743, t = 15.413, p < 0.001). 96.0% of the variance in INT and 54.7% of the variance in PU could all be explained by the model. Conclusion: The findings show that perceived usefulness and perceived ease of use both shape the users’ intention to adopt the Balinese Script Keyboard. Other than that, ease of use also correlates strongly with perceived usefulness by strengthening it, suggesting that usability is a key factor in the acceptance of digital tools for complex traditional scripts. This study provides early empirical support for the application of TAM to minority-script digital technologies. It is noted that broader user groups, additional adoption factors, and examination of longer-term use in real-world settings are all to be included in future research.   Keywords: Balinese script digital tool, Technology acceptance model, Perceived Usefulness, Perceived Ease of Use, Usage Intention
HAXE: Attention–Rationale Alignment Framework for Explainable Hate Speech Detection in Transformer Models James Alvin Dhanardi; Fredy Purnomo; Anang Prasetyo
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.414-427

Abstract

Background: The surge of social media content in the modern era required an automated content moderation system to filter out hate speech and inappropriate content. Transformer-based models have been the main approach to this problem. However, it is important to ensure explainability and fairness, as the decision may not align with human reasoning and can create unintended bias. Objective: This study aims to investigate whether integrating transformer-based models with human-annotated rationales can drive the model to create better explainability and fairness in its output. Methods: We propose HAXE, a ranking-based attention-rational alignment framework that supervises attention with relative-importance constraints to prevent rationale distortion. Experiments were conducted on the HateXplain data using BERT, DistilBERT, and DeBERTa as the transformer models. Model performance is evaluated in the classification, explainability, and fairness domains, as well in the attention entropy analysis. Results: HAXE improves explainability across all models, as evidenced by the increased plausibility and faithfulness metrics score, with the largest gains observed in DistilBERT and DeBERTa. However, we observed that fairness effects are architecture-dependent, where DistilBERT and DeBERTa improved, whereas BERT showed degradation. Attention entropy analysis shows that BERT undergoes the largest entropy reduction, approximately 16%, compared with DistilBERT and DeBERTa, which is 10–13% greater sensitivity to attention collapse under the HAXE training objective. Conclusion: The results demonstrate that attention-rationale alignment strengthens explanation quality, but its fairness effects differ between model architectures. These trade-offs are required for supervision strategies that can adapt and be robust to different architectures. Future work should explore ER alignment, other supervision strategies, and evaluation across different datasets.   Keywords: Explainable AI, Fairness in NLP, Hate Speech Detection, Attention Supervision, Transformer Models
Leveraging Data Analytics Across Digital Product Development Stages : A Systematic Review and Conceptual Framework Noor Alamsyah; Bundit Thanasopon; Pornsuree Jamsri
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.392-413

Abstract

Background: Data analytics (DA) is a field that has expanded greatly and is an important tool for digital product development, and has captured researcher and practitioner interest. Nevertheless, from an Information Systems and Business Intelligence (IS/BI) view, there is still a lack of knowledge regarding the role that data analytics plays in the digital product development lifecycle for decision making. The volume and the complexity of digital product innovation and analytics continues to increase, thereby further enhancing the need for existing knowledge to be consolidated in this area. Objective: This research systematically reviewed the latest academic research in the field of data analytics in digital product development and explain the specific uses of data analytics in the different stages of digital product development for supporting decision making and innovation activities. Methods: This study followed the systematic literature review method through ScienceDirect, IEEE Xplore and Emerald databases. Upon initial search, 1,554 articles were found; 33 relevant articles were identified after a structured screening and eligibility assessment of the articles in line with Kitchenham's protocol. Results: The results reveal the differentiated use of data analytics in the various stages, namely opportunity identification through text mining, feasibility assessment through predictive modelling, prototyping through digital twins and generative design and market responsiveness through predictive analytics and recommender systems. Even with analytical processes and concepts in place, companies often face challenges due to data integration issues, analytical skill, and organizational preparedness. The data quality issues, algorithmic bias, ethical considerations, or lack of algorithm transparency all contribute to these limitations, hindering the full potential of data analytics in digital product development. Conclusion: To realize more effective and aligned outcomes of innovation, it is important to understand how data analytics can assist in decision making throughout the digital product development lifecycle. This research is valuable for researchers and practitioners as it provides a structured conceptual framework for association of analytics initiatives with digital product development goals. There is potential for this work to be extended in future studies, involving the creation and validation of scalable analytics frameworks in various organisational contexts.   Keywords: Systematic Literature Review (SLR) , Data Analytics, Digital Product Development, Innovation Management, Artificial Intelligence

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